Image-guided surgery (hereinafter “IGS”) involves patient-specific anatomical images pre-operatively acquired that spatially localize pathology, digitization technology that allows the identification and tracking of targeted points of interest in a patient's physical space in an operating room (hereinafter “OR”), and alignments of the patient-specific images to the patient's physical space in the OR such that the digitization technology can be referenced to the patient-specific images and used for guidance during surgery. Central to the IGS is the method of registering an image space (a coordinate system corresponding to the pre-operative images) to a physical space (a coordinate system corresponding to the intra-operative anatomy of the patient). Once the registration is performed, all pre-operative planning and acquired data related to the patient's anatomy could be displayed intra-operatively to a surgeon and used for assistance in surgical guidance and treatment.
Over to past years, a variety of registration methods have been developed. Among them, a point-based registration (hereinafter “PBR”) has been mostly characterized and thoroughly examined, whereby landmarks are localized in patient's image volumes and aligned with corresponding landmarks digitized in physical space of the patient intra-operatively. The landmarks, or fiducials, can be either natural structures such as a nose bridge of the patient, or synthetic components such as small cylindrical markers adhered to the skin of the patient or markers implanted into the skull of the patient prior to image acquisitions [1,2]. Further analysis of configurations of fiducial markers, optimum marker numbers, and effects on target localization error has been reported [2]. The PBR technique has proven clinically accurate and useful. However, utilization of the PBR method requires a preliminary surgery for implantation of the fiducial markers to predetermined positions in a patient's anatomy.
Another technique for the registration is accomplished by identifying two geometric surfaces that are the same in an image space and a physical space of a patient, respectively, and aligning them between the two spaces. The ability to acquire surface data using a probe, such as optical probe, electromagnetic probe, and/or ultrasound probe, and lasers [3–7] in conjunction with surface extraction algorithms applied to imaging data has led to new methods of surface based registrations [8]. The primary difference between the surface-based registration and the PBR is that the surface based registration does not require a one-to-one point correspondence. On the other hand, an averaging effect in the surface-based registration serves to reduce uncorrelated localization error generated during the acquisition of spatially well-resolved surface data. However, the surface based alignment techniques are limited with facts, for example, scalps lack geometric specificity, and skin surfaces may deform due to intra-operative drugs or procedural retraction [9]. An alternative registration technique, less commonly used for IGS purposes, is an intensity-based or volume registration approach [2], which is usually applied for alignments of a source image volume to a target image volume.
However, recent studies have shown limitations in accuracy with current image-guided procedures. The discrepancy observed is a by-product of the rigid-body assumptions and techniques used during the registration process. Specifically, with neurosurgery, registration is provided by markers attached to the skull of a patient or on the skin surrounding the skull of a patient, where soft-tissue deformations of the brain during surgery may result in significant errors in aligning a pre-operative image space to an actual physical space. One of the earliest observed instances of the error was reported by Kelly et al. [10]. More recently, Nauta has measured this shift that is of an order of 5 mm [11]. Subsequent investigations in intra-operative brain surface movements have shown that an average deformation for brain shifts is about 1 cm. Moreover, predispositions for brain movement in the direction of gravity have been investigated [12, 13].
This has lead studies to develop methods and techniques that can compensate for intra-operative brain shifts. One of the methods includes the use of conventional imaging modalities during surgery, i.e. intra-operative computed tomography (hereinafter “iCT”), intra-operative magnetic resonance (hereinafter “iMR”), and/or intra-operative ultrasound (hereinafter “iUS”) imaging. When available, intra-operative images are registered to pre-operative images using a number of nonrigid intra-modal and/or inter-modal registration methods. In the 1980s, there was a significant effort to incorporate iCT during surgery as a means for acquiring intra-operative image series. However, dose considerations of repeatedly using computed tomography (hereinafter “CT”) scanning in the OR have hindered adoption of the iCT technique [14]. More recently, several medical centers have explored the use of iMR imaging for data acquisition and shift compensation [15–18] and have developed elegant and sophisticated methods for visualization in the OR [3, 19, 20]. Although conceptually appealing, the exorbitant cost and cumbersome nature of such a system (e.g., need for a MR compatible OR) have left their widespread adoption uncertain. In addition to these logistical concerns, recent reports have demonstrated potential problems related to surgically induced contrast enhancement that could be often confused with contrast-enhancing residual tumor [21], and image distortions from susceptibility and/or eddy current artifacts related to the presence of MR compatible Yasargil clips for aneurysm clipping procedures [22]. An alternative to iCT and iMR imaging is the use of iUS [23–26], where locally reconstructed iUS image volumes may provide a real-time guidance feedback. However, the quality of the iUS images over the course of surgery limits their effectiveness in shift compensation.
A possible alternative to high-cost intra-operative imaging is to use computational methods to compensate for brain shifts in IGS. A strategy for using computational methods to correct for brain shifts in neurosurgery was highlighted by Roberts et al. [27]. Rapidly acquiring minimally invasive data that describes changes in brain geometry during surgery is necessary to develop a computational approach that accounts for brain deformations. In these methods, intra-operative surface data are combined with a statistical and/or mathematical model of the soft-tissue mechanics that describe brain deformation [27]. Physical models have been successfully used to reconstitute 70% to 80% of the shift occurring under loads similar to a clinical setting. A detailed work regarding the fidelity of such computations within animal and human systems has been reported [28, 29]. Registrations of multimodality images by elastic matching technique have also been studied [30, 31]. Deformable templates for large deformation warping of images have been utilized [32]. However, the computational methods may not be able to effectively predict the extent of tumor margins. On the other hand, the next important question is how to integrate intra-operative measurements within a framework that is feasible for the OR use. A makeshift approximation is to apply all measurements as displacement boundary conditions within the model and move forward. This approach has been utilized by Ferrant et al. [49] within the context of iMR and treats the computational model as an interpolator. Although intuitive, this approach may not work for a model-based compensation platform in medical centers without an iMR system.
Therefore, a heretofore unaddressed need exists in the art to address the aforementioned deficiencies and inadequacies.